量子ゲート平均忠実度測定方法としての Randomized Benchmarking の有効性と課題 (量子統計モデリングのための基盤構築)

Takahiro Tsunoda

Takanori Sugiyama

量子計算機とは量子系の性質を積極的に利用して計算を行う装置の1種である。量子計算機を用いることにより、様々な問題の解を既存の計算機よりも高速に求められることが理論的に示されている。量子計算機の実用化のためには, 量子誤り訂正符号とよばれるエラー対策方法を導入し、その上でシステムの大規模化と演算のさらなる高精度化が必要となる。実装デバイスの演算精度を評価する手法として, 量子トモグラフィやRandomiaed Benchmarkingなど様々な統計的手法が提案され実験でも利用されている。しかしながら、現在提案されているどの手法にも長所と短所があり、量子誤り訂正に利用される演算の精度評価手法としてはどれも不十分である。本稿では、精度評価に利用される統計的手法の一つである量子トモグラフィの基本事項と課題、そして課題解決に向けた近年の試みについて解説する。

Jordan Hines, Marie Lu, Ravi Naik +10 more

Many-qubit errors such as crosstalk are not apparent in low-width circuits Randomized Benchmarking (RB) [1] and Random Circuit Sampling (RCS) [2] characterize average gate performance • Large gate overhead (RB) • High computational cost for universal gate sets (RB, RCS) Circuit mirroring [3]: Flexible technique for designing scalable benchmarks Scalable Randomized Benchmarking

Timothy J Proctor, Kenneth Rudinger, Robin Blume-Kohout +2 more

Goutam Paul

Benchmarking noise in quantum gates is crucial for developing dependable quantum computers, as noise directly affects gate fidelity and computational accuracy. Randomized benchmarking (RB) protocols, which evaluate performance through random gate sequences, provide a reliable way to quantify average error rates due to their scalability and robustness in averaging out complex, device-specific noise. In this talk, we will discuss several variants of randomized benchmarking protocols including an overview of our recent work in this area.

Jonas Helsen, Joel J. Wallman, Steven T. Flammia +1 more

Randomized benchmarking (RB) is an efficient and robust method to characterize gate errors in quantum circuits. Averaging over random sequences of gates leads to estimates of gate errors in terms of the average fidelity. These estimates are isolated from the state preparation and measurement errors that plague other methods such as channel tomography and direct fidelity estimation. A decisive factor in the feasibility of randomized benchmarking is the number of sampled sequences required to obtain rigorous confidence intervals. Previous bounds were either prohibitively loose or required the number of sampled sequences to scale exponentially with the number of qubits in order to obtain a fixed confidence interval at a fixed error rate. Here, we show that, with a small adaptation to the randomized benchmarking procedure, the number of sampled sequences required for a fixed confidence interval is dramatically smaller than could previously be justified. In particular, we show that the number of sampled sequences required is essentially independent of the number of qubits and scales favorably with the average error rate of the system under investigation. We also investigate the fitting procedure inherent to randomized benchmarking in light of our results and find that standard methods such as ordinary least squares optimization can give misleading results. We therefore recommend moving to more sophisticated fitting methods such as iteratively reweighted least squares optimization. Our results bring rigorous randomized benchmarking on systems with many qubits into the realm of experimental feasibility.